It is no secret that America’s infrastructure is aging and hurting. The most recent American Society of Civil Engineer’s (ASCE) 2017 “Infrastructure Report Card” measured 16 categories and reported an overall D+ grade.

Many American infrastructure systems currently in place were built decades ago, and recent studies indicate that delays and rising maintenance costs are hindering economic performance. In fact, the cost of improving U.S. infrastructure has increased by over 250 percent since 2001 to $4.59 trillion.

Even more importantly, however, engineers are raising safety concerns, warning that many bridges are structurally unsound and that antiquated drinking-water and wastewater systems pose risks to public health.

This is a shame, because with currently available sensor technologies we should be able to ”micro-manage” our aging infrastructure systems much more effectively than ever before. Big data and related technologies can help lay the foundation for the future adoption of artificial intelligence and robotics that would create a zero failure, highly resilient and sustainable infrastructure system. A smart infrastructure system can become a reality here in the U.S. during our lifetimes—as long as we use the right people, processes and technologies.

For example, the utilization of sensors in infrastructure systems will include wireless sensor networks (WSN) as a component of the “internet of things” and will be designed around the capabilities of autonomous nodes. Each node in the network will integrate specific sensing capabilities with communication, data processing and, of course, power supply. Data from these nodes when acquired, integrated and managed for insights as well as prescriptive and predictive analytics, will enable superior capabilities in managing the infrastructure systems at a very high standard.

When sensors are engaged in the various infrastructure categories (16 as of 2017) and adequately managed, an opportunity is created for “micromanaging” individual elements, including actual nuts and bolts. We could keep track of their deteriorating tensile strength, along with their maintenance cycles, and thus make it easier and faster to get the right information to the right people at the right time—all while lowering the workload for engineering staff.

Naturally, this will create an enormous amount of data. In the past, organizing large amounts of engineering data was economically impossible, but today, when integrated with traditional engineering practices, big data can improve the effectiveness as well as the efficiencies involved in engineering process systems without being unsustainably expensive.

When “person-centric” data is managed well, it is likely to create financial valuations in the billions of dollars as demonstrated by the private sector in the form of the most valuable technology companies on the Fortune 500 list. In fact, the companies that manage their data best have ended up becoming the most valuable companies on the planet. There is no reason why infrastructure agencies should not pursue this path of depending on data to increase their financial valuation and at the same time provide safer, efficient and effective infrastructure services.

The starting point is to understand that there is no substitute for sound processes and that technology advancements in themselves are not necessarily the only solution. Regardless of the promise of this technology, the involved people must be engaged to develop the competencies in order to act on this data effectively and in a timely manner. In the end, that is what will make all the difference.

Fortunately, advances in sensor technology along with available big data technologies and solutions can provide an opportunity to aggressively close the trillions of dollars of fiscal gap to improve the infrastructure through the micromanagement of every involved component ranging from nuts and bolts to trusses and bridges.

Our world is an orchestration of people, processes and technologies. The American infrastructure systems are no exception. Fortunately, data connects the dots between them all.

The views expressed are those of the author(s) and are not necessarily those of Scientific American.

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